Week7-2_Policeman

library(ggplot2)
library(maps)
library(ggmap)

setwd("D:/Google Drive/College/4-The Analytics Edge/data7")

murders = read.csv("murders.csv")
mvt = read.csv("mvt.csv")
summary(mvt)

##              Date           Latitude       Longitude     
##  5/16/08 0:00  :    11   Min.   :41.64   Min.   :-87.93  
##  10/17/01 22:00:    10   1st Qu.:41.77   1st Qu.:-87.72  
##  4/13/04 21:00 :    10   Median :41.85   Median :-87.68  
##  9/17/05 22:00 :    10   Mean   :41.84   Mean   :-87.68  
##  10/12/01 22:00:     9   3rd Qu.:41.92   3rd Qu.:-87.64  
##  10/13/01 22:00:     9   Max.   :42.02   Max.   :-87.52  
##  (Other)       :191582   NA's   :2276    NA's   :2276

str(mvt)

## 'data.frame':    191641 obs. of  3 variables:
##  $ Date     : Factor w/ 131680 levels "1/1/01 0:01",..: 42824 42823 42823 42823 42822 42821 42820 42819 42817 42816 ...
##  $ Latitude : num  41.8 41.9 42 41.8 41.8 ...
##  $ Longitude: num  -87.6 -87.7 -87.8 -87.7 -87.6 ...

Clean data

# Convert the Date variable to a format that R will recognize:
mvt$Date = strptime(mvt$Date, format="%m/%d/%y %H:%M")

# Extract the hour and the day of the week:
mvt$Weekday = weekdays(mvt$Date)
mvt$Hour = mvt$Date$hour

# Let's take a look at the structure of our data again:
str(mvt)

## 'data.frame':    191641 obs. of  5 variables:
##  $ Date     : POSIXlt, format: "2012-12-31 23:15:00" "2012-12-31 22:00:00" ...
##  $ Latitude : num  41.8 41.9 42 41.8 41.8 ...
##  $ Longitude: num  -87.6 -87.7 -87.8 -87.7 -87.6 ...
##  $ Weekday  : chr  "Monday" "Monday" "Monday" "Monday" ...
##  $ Hour     : int  23 22 22 22 21 20 20 20 19 18 ...

# Create a simple line plot - need the total number of crimes on each day of the week. We can get this information by creating a table:
table(mvt$Weekday)

## 
##    Friday    Monday  Saturday    Sunday  Thursday   Tuesday Wednesday 
##     29284     27397     27118     26316     27319     26791     27416

# Save this table as a data frame:
WeekdayCounts = as.data.frame(table(mvt$Weekday))
str(WeekdayCounts) 

## 'data.frame':    7 obs. of  2 variables:
##  $ Var1: Factor w/ 7 levels "Friday","Monday",..: 1 2 3 4 5 6 7
##  $ Freq: int  29284 27397 27118 26316 27319 26791 27416

Build a plot

# Create our plot,  group = 1 make it to one line.
ggplot(WeekdayCounts, aes(x=Var1, y=Freq)) + geom_line(aes(group=1))  

# Make the "Var1" variable an chronological order instead of an alphabetical order.
WeekdayCounts$Var1 = factor(WeekdayCounts$Var1, ordered=TRUE, levels=c("Sunday", "Monday", "Tuesday", "Wednesday", "Thursday", "Friday","Saturday"))

# Try again:
ggplot(WeekdayCounts, aes(x=Var1, y=Freq)) + 
    geom_line(aes(group=1))

# Change our x and y labels:
ggplot(WeekdayCounts, aes(x=Var1, y=Freq)) + geom_line(aes(group=1), linetype=2, alpha=0.3) + xlab("Day of the Week") + 
    ylab("Total Motor Vehicle Thefts")

Adding Weekday variable with heatmap

Clean data

# Create a counts table for the weekday and hour:
table(mvt$Weekday, mvt$Hour)

##            
##                0    1    2    3    4    5    6    7    8    9   10   11
##   Friday    1873  932  743  560  473  602  839 1203 1268 1286  938  822
##   Monday    1900  825  712  527  415  542  772 1123 1323 1235  971  737
##   Saturday  2050 1267  985  836  652  508  541  650  858 1039  946  789
##   Sunday    2028 1236 1019  838  607  461  478  483  615  864  884  787
##   Thursday  1856  816  696  508  400  534  799 1135 1298 1301  932  731
##   Tuesday   1691  777  603  464  414  520  845 1118 1175 1174  948  786
##   Wednesday 1814  790  619  469  396  561  862 1140 1329 1237  947  763
##            
##               12   13   14   15   16   17   18   19   20   21   22   23
##   Friday    1207  857  937 1140 1165 1318 1623 1652 1736 1881 2308 1921
##   Monday    1129  824  958 1059 1136 1252 1518 1503 1622 1815 2009 1490
##   Saturday  1204  767  963 1086 1055 1084 1348 1390 1570 1702 2078 1750
##   Sunday    1192  789  959 1037 1083 1160 1389 1342 1706 1696 2079 1584
##   Thursday  1093  752  831 1044 1131 1258 1510 1537 1668 1776 2134 1579
##   Tuesday   1108  762  908 1071 1090 1274 1553 1496 1696 1816 2044 1458
##   Wednesday 1225  804  863 1075 1076 1289 1580 1507 1718 1748 2093 1511

# Save this to a data frame:
DayHourCounts = as.data.frame(table(mvt$Weekday, mvt$Hour))

str(DayHourCounts)

## 'data.frame':    168 obs. of  3 variables:
##  $ Var1: Factor w/ 7 levels "Friday","Monday",..: 1 2 3 4 5 6 7 1 2 3 ...
##  $ Var2: Factor w/ 24 levels "0","1","2","3",..: 1 1 1 1 1 1 1 2 2 2 ...
##  $ Freq: int  1873 1900 2050 2028 1856 1691 1814 932 825 1267 ...

# Convert the second variable, Var2, from factor from numeric
DayHourCounts$Hour = as.numeric(as.character(DayHourCounts$Var2))

Build a plot

# Create out plot:
ggplot(DayHourCounts, aes(x=Hour, y=Freq)) + 
    geom_line(aes(group=Var1))

# Change the colors
ggplot(DayHourCounts, aes(x=Hour, y=Freq)) + 
    geom_line(aes(group=Var1, color=Var1), size=2)

# Separate the weekends from the weekdays:
DayHourCounts$Type = ifelse(
    (DayHourCounts$Var1 == "Sunday") | (DayHourCounts$Var1 == "Saturday"), 
    "Weekend", "Weekday")

# Redo our plot, this time coloring by Type:
ggplot(DayHourCounts, aes(x=Hour, y=Freq)) + 
    geom_line(aes(group=Var1, color=Type), size=2) 

# Make the lines a little transparent:
ggplot(DayHourCounts, aes(x=Hour, y=Freq)) + 
    geom_line(aes(group=Var1, color=Type), size=2, alpha=0.5) 

Build a heat map

# Fix the order of the days:
DayHourCounts$Var1 = factor(DayHourCounts$Var1, ordered=TRUE, levels=c("Monday", "Tuesday", "Wednesday", "Thursday", "Friday", "Saturday", "Sunday"))

# Make a heatmap:
ggplot(DayHourCounts, aes(x=Hour, y=Var1)) + 
    geom_tile(aes(fill=Freq))

# Change the label on the legend, and get rid of the y-label:
ggplot(DayHourCounts, aes(x=Hour, y=Var1)) + 
    geom_tile(aes(fill=Freq)) + 
    scale_fill_gradient(name="Total MV Thefts") +
    theme(axis.title.y=element_blank())

# Change the color scheme
ggplot(DayHourCounts, aes(x=Hour, y=Var1)) + 
    geom_tile(aes(fill=Freq)) + 
    scale_fill_gradient(name="Total MV Thefts", low="white", high="red") +
    theme(axis.title.y = element_blank())

Build a google map

Clean data

# Load a map of Chicago into R:
chicago = get_map(location="chicago", zoom=11)

## Map from URL : http://maps.googleapis.com/maps/api/staticmap?center=chicago&zoom=11&size=640x640&scale=2&maptype=terrain&language=en-EN&sensor=false
## Information from URL : http://maps.googleapis.com/maps/api/geocode/json?address=chicago&sensor=false

ggmap(chicago)

# Plot the first 100 motor vehicle thefts:
ggmap(chicago) + geom_point(data=mvt[1:100,], aes(x=Longitude, y=Latitude))

## Warning: Removed 7 rows containing missing values (geom_point).

# Round our latitude and longitude to 2 digits of accuracy, and create a crime counts data frame for each area:
LatLonCounts = as.data.frame(table(round(mvt$Longitude,2), round(mvt$Latitude,2)))
str(LatLonCounts)

## 'data.frame':    1638 obs. of  3 variables:
##  $ Var1: Factor w/ 42 levels "-87.93","-87.92",..: 1 2 3 4 5 6 7 8 9 10 ...
##  $ Var2: Factor w/ 39 levels "41.64","41.65",..: 1 1 1 1 1 1 1 1 1 1 ...
##  $ Freq: int  0 0 0 0 0 0 0 0 0 0 ...

# Convert our Longitude and Latitude variable from factor var. to numeric var.
LatLonCounts$Long = as.numeric(as.character(LatLonCounts$Var1))
LatLonCounts$Lat = as.numeric(as.character(LatLonCounts$Var2))

# Plot these points on our map:
ggmap(chicago) + 
    geom_point(data=LatLonCounts, aes(x=Long, y=Lat, color=Freq, size=Freq))

## Warning: Removed 615 rows containing missing values (geom_point).

# Change the color scheme:
ggmap(chicago) + 
    geom_point(data=LatLonCounts, aes(x=Long, y=Lat, color=Freq, size=Freq)) +
    scale_colour_gradient(low="yellow", high="red")

## Warning: Removed 615 rows containing missing values (geom_point).

# We can also use the geom_tile geometry
ggmap(chicago) + geom_tile(data=LatLonCounts, aes(x=Long, y=Lat, alpha=Freq), fill="red")

# Remove red squre in the water
LatLonCounts2 = subset(LatLonCounts, Freq > 0)
ggmap(chicago) + 
    geom_tile(data=LatLonCounts2, aes(x=Long, y=Lat, alpha=Freq), fill="red")

FBI

Load map

str(murders)

## 'data.frame':    51 obs. of  6 variables:
##  $ State            : Factor w/ 51 levels "Alabama","Alaska",..: 1 2 3 4 5 6 7 8 9 10 ...
##  $ Population       : int  4779736 710231 6392017 2915918 37253956 5029196 3574097 897934 601723 19687653 ...
##  $ PopulationDensity: num  94.65 1.26 57.05 56.43 244.2 ...
##  $ Murders          : int  199 31 352 130 1811 117 131 48 131 987 ...
##  $ GunMurders       : int  135 19 232 93 1257 65 97 38 99 669 ...
##  $ GunOwnership     : num  0.517 0.578 0.311 0.553 0.213 0.347 0.167 0.255 0.036 0.245 ...

# Load the map of the US
statesMap = map_data("state")
str(statesMap)

## 'data.frame':    15537 obs. of  6 variables:
##  $ long     : num  -87.5 -87.5 -87.5 -87.5 -87.6 ...
##  $ lat      : num  30.4 30.4 30.4 30.3 30.3 ...
##  $ group    : num  1 1 1 1 1 1 1 1 1 1 ...
##  $ order    : int  1 2 3 4 5 6 7 8 9 10 ...
##  $ region   : chr  "alabama" "alabama" "alabama" "alabama" ...
##  $ subregion: chr  NA NA NA NA ...

# Plot the map:
ggplot(statesMap, aes(x=long, y=lat, group=group)) + 
    geom_polygon(fill="white", color="black") 

Clean data

# Preprocess to merge the two data frame by
# Create a new variable called region with the lowercase names to match the statesMap:
murders$region = tolower(murders$State)

# Join the statesMap data and the murders data into one dataframe:
# by="region" the identifier to use to merge the rows.
murderMap = merge(statesMap, murders, by="region")
str(murderMap)

## 'data.frame':    15537 obs. of  12 variables:
##  $ region           : chr  "alabama" "alabama" "alabama" "alabama" ...
##  $ long             : num  -87.5 -87.5 -87.5 -87.5 -87.6 ...
##  $ lat              : num  30.4 30.4 30.4 30.3 30.3 ...
##  $ group            : num  1 1 1 1 1 1 1 1 1 1 ...
##  $ order            : int  1 2 3 4 5 6 7 8 9 10 ...
##  $ subregion        : chr  NA NA NA NA ...
##  $ State            : Factor w/ 51 levels "Alabama","Alaska",..: 1 1 1 1 1 1 1 1 1 1 ...
##  $ Population       : int  4779736 4779736 4779736 4779736 4779736 4779736 4779736 4779736 4779736 4779736 ...
##  $ PopulationDensity: num  94.7 94.7 94.7 94.7 94.7 ...
##  $ Murders          : int  199 199 199 199 199 199 199 199 199 199 ...
##  $ GunMurders       : int  135 135 135 135 135 135 135 135 135 135 ...
##  $ GunOwnership     : num  0.517 0.517 0.517 0.517 0.517 0.517 0.517 0.517 0.517 0.517 ...

Build a google map by Murders, and Population

# Plot the number of murder on our map of the United States:
ggplot(murderMap, aes(x=long, y=lat, group=group, fill=Murders)) +
    geom_polygon(color="black") + 
    scale_fill_gradient(low="black", high="red", guide="legend")

# Plot a map of the population:
ggplot(murderMap, aes(x=long, y=lat, group=group, fill=Population)) +
    geom_polygon(color="black") + 
    scale_fill_gradient(low="black", high="red", guide="legend")

We have a population map here which looks exactly the same as our murders map. So we need to plot the murder rate instead of the number of murders to make sure we’re not just plotting a population map.

Build a google map by murders rate

# Create a new variable that is the number of murders per 100,000 population:
murderMap$MurderRate = murderMap$Murders / murderMap$Population * 100000

# Redo our plot with murder rate:
ggplot(murderMap, aes(x=long, y=lat, group=group, fill=MurderRate)) + 
    geom_polygon(color="black") + 
    scale_fill_gradient(low="black", high="red", guide="legend")

# Redo the plot, removing any states with murder rates above 10:
ggplot(murderMap, aes(x=long, y=lat, group=group, fill=MurderRate)) +
    geom_polygon(color="black") + 
    scale_fill_gradient(low="black", high="red", guide="legend", limits=c(0,10))

Build a google map by gun owndership rate

# Create a new variable that is the number of gun owner per 100,000 population:
murderMap$GunOwnerRate = murderMap$GunOwnership / murderMap$Population * 100000

# Redo our plot with murder rate:
ggplot(murderMap, aes(x=long, y=lat, group=group, fill=GunOwnerRate)) +
    geom_polygon(color="black") + 
    scale_fill_gradient(low="black", high="red", guide="legend")

heatmap can visualize data that will be too big for a talbe.
Plotting data on maps is much more effeict that a table for location based on data.